A $1,200 sofa takes 4-8 weeks to buy. Attribution that respects the journey.
AI agents that learn long consideration windows, room-by-room cross-sell, and the high-AOV mistakes that cost $15K in a single week.
Last-click gives 100% to Direct. Parker distributes credit across the full journey.
Discovery
ResearchRe-engagement→ Cut Meta upper-funnel spend, Google conversions tend to collapse 3 to 5 weeks later. Last-click can't see this dependency.
Last-click sees the final touch. Parker sees the journey. Exact attribution split depends on your category and customer behavior. (architecture target)
Four reasons home math needs a wider window.
High AOV, long research, room-by-room cross-sell, and seasonal moves all break standard click attribution. The mistakes are bigger because the orders are bigger.
Long research kills last-click attribution
Customer sees Instagram in week 1, browses Google in week 3, clicks retargeting in week 5, buys. Last-click gives Google a journey that started on Meta.
High AOV makes every mistake expensive
Average order at $400 to $2,000 means scaling the wrong campaign wastes $15K in a week, and retracting takes another week to stabilize CPMs.
Room-by-room cross-sell is invisible
Sofa in week 1, rug in week 6, lighting in month 3. One customer furnishing one room reads as three independent acquisitions.
Move-in spikes are unpredictable
Spring and fall move-in waves are predictable, but local housing patterns aren't. The dashboard catches the demand shift two weeks late.
Read the journey. Quantify the cross-sell.
Felix forecasts demand and replenishment. Sam models high-AOV scaling. Parker reconciles the long-window attribution. Maya holds the room-by-room memory.
Felix
Forecasting
Forecasts revenue and demand at high-AOV with seasonal move-in cycles priced in. Predicts cross-sell timing room-by-room. Architecture target: budget recommendations auto-adjust for spring and fall move-in waves.
Move-in-aware forecasting.
Sam
Scenario Testing
Models high-AOV scaling scenarios with the hard constraint that one wrong move costs $15K. Architecture target: simulate scaling decisions before they hit a $400-to-$2,000 order book.
High-AOV scaling, modeled.
Parker
Attribution
Uses extended attribution windows that match actual furniture buying behavior. Surfaces upstream Meta awareness that drove downstream Google conversions. Architecture target: credit the journey, not the last click.
Journey-aware attribution.
Maya
Memory & Context
Holds the room-by-room cross-sell memory across customers. Recalls which sofa buyers became rug buyers became lighting buyers and on what cadence. Architecture target: cross-sell campaigns timed to where the customer is in the room.
Room-by-room memory.
The other three agents fill out the workforce. See all seven →.
Concrete deltas. Architecture targets for home & living.
Four metrics targeted by the 14-day pilot structure. Exact numbers depend on AOV, category mix, and current attribution stack.
Architecture target: Parker uses extended windows matching 4-to-8 week furniture purchase decisions.
Architecture target: Maya tracks the sofa-to-rug-to-lighting sequence; Felix predicts the timing.
Architecture target: Dex catches CPM and CPA drift inside 24 hours, before $15K compounds.
Architecture target across the 9-month pilot structure. Felix surfaces move-in waves before they hit the dashboard.
Questions home & living teams ask
How does Cresva handle high-AOV attribution?
Parker uses extended windows that match 4-to-8 week furniture purchase decisions. Architecture target: stop under-crediting awareness campaigns that started journeys weeks before the click.
Can Cresva track room-by-room cross-sell?
Yes. Maya tracks cross-sell sequences (sofa → rug → lighting → art) and Felix predicts the timing. Architecture target: campaigns that meet the customer at the right moment in the room.
How does Cresva handle seasonal furniture demand?
Felix learns the brand's specific seasonal patterns: spring and fall move-in spikes, holiday gifting, renovation cycles. Architecture target: budget recommendations adjust before the wave hits.
Does Cresva work for made-to-order and long lead times?
Yes. Felix accounts for lead time in forecasting; Parker's attribution handles the gap between ad exposure, order, and fulfillment.
How fast can a home brand get started?
Five minutes via OAuth: Shopify, Meta, Google, TikTok. First insights inside 48 hours. Forecasts sharpen across two seasonal cycles.
Home & living view not the right fit?
Ready when you are
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Looking for a deep dive? See Felix forecasts →, Parker debiases → or Maya remembers →.